Overview

Dataset statistics

Number of variables32
Number of observations124462
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.6 MiB
Average record size in memory123.0 B

Variable types

Categorical22
DateTime1
Text1
Numeric8

Alerts

metric7_log is highly overall correlated with metric8_logHigh correlation
metric8_log is highly overall correlated with metric7_logHigh correlation
failure is highly imbalanced (99.0%)Imbalance
D__Z1F1 is highly imbalanced (68.0%)Imbalance
D__Z1F2 is highly imbalanced (97.9%)Imbalance
MoW_5 is highly imbalanced (65.6%)Imbalance
metric7_log is highly imbalanced (90.8%)Imbalance
metric8_log is highly imbalanced (90.8%)Imbalance
metric2_log has 118082 (94.9%) zerosZeros
metric3_log has 115331 (92.7%) zerosZeros
metric4_log has 115130 (92.5%) zerosZeros
metric9_log has 97332 (78.2%) zerosZeros

Reproduction

Analysis started2023-06-22 08:12:04.034364
Analysis finished2023-06-22 08:12:17.331147
Duration13.3 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

failure
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
124356 
1
 
106

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

Length

2023-06-22T16:12:17.391263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:17.638573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124356
99.9%
1 106
 
0.1%

date
Date

Distinct303
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
Minimum2015-01-01 00:00:00
Maximum2015-10-31 00:00:00
2023-06-22T16:12:17.720557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:17.826459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

device
Text

Distinct1169
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
2023-06-22T16:12:17.985396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters995696
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowS1F01085
2nd rowW1F0Y13C
3rd rowW1F0XKWR
4th rowW1F0X7QX
5th rowW1F0X7PR
ValueCountFrequency (%)
z1f0kkn4 303
 
0.2%
w1f05x69 303
 
0.2%
z1f0qlc1 303
 
0.2%
w1f0g9t7 303
 
0.2%
w1f0fy92 303
 
0.2%
z1f0qk05 303
 
0.2%
z1f0ql3n 303
 
0.2%
z1f0q8rt 303
 
0.2%
s1f0h6jg 303
 
0.2%
w1f0feh7 303
 
0.2%
Other values (1159) 121432
97.6%
2023-06-22T16:12:18.234184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 191959
19.3%
F 138423
13.9%
0 91178
 
9.2%
S 76236
 
7.7%
W 54962
 
5.5%
Z 39870
 
4.0%
L 25055
 
2.5%
3 23980
 
2.4%
K 18752
 
1.9%
B 17804
 
1.8%
Other values (24) 317477
31.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 593520
59.6%
Decimal Number 402175
40.4%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 138423
23.3%
S 76236
12.8%
W 54962
 
9.3%
Z 39870
 
6.7%
L 25055
 
4.2%
K 18752
 
3.2%
B 17804
 
3.0%
R 17712
 
3.0%
J 17504
 
2.9%
G 17261
 
2.9%
Other values (13) 169941
28.6%
Decimal Number
ValueCountFrequency (%)
1 191959
47.7%
0 91178
22.7%
3 23980
 
6.0%
6 15877
 
3.9%
5 15299
 
3.8%
2 14010
 
3.5%
4 13676
 
3.4%
7 12221
 
3.0%
9 12038
 
3.0%
8 11937
 
3.0%
Lowercase Letter
ValueCountFrequency (%)
ÿ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 593521
59.6%
Common 402175
40.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 138423
23.3%
S 76236
12.8%
W 54962
 
9.3%
Z 39870
 
6.7%
L 25055
 
4.2%
K 18752
 
3.2%
B 17804
 
3.0%
R 17712
 
3.0%
J 17504
 
2.9%
G 17261
 
2.9%
Other values (14) 169942
28.6%
Common
ValueCountFrequency (%)
1 191959
47.7%
0 91178
22.7%
3 23980
 
6.0%
6 15877
 
3.9%
5 15299
 
3.8%
2 14010
 
3.5%
4 13676
 
3.4%
7 12221
 
3.0%
9 12038
 
3.0%
8 11937
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 995695
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 191959
19.3%
F 138423
13.9%
0 91178
 
9.2%
S 76236
 
7.7%
W 54962
 
5.5%
Z 39870
 
4.0%
L 25055
 
2.5%
3 23980
 
2.4%
K 18752
 
1.9%
B 17804
 
1.8%
Other values (23) 317476
31.9%
None
ValueCountFrequency (%)
ÿ 1
100.0%

metric1
Real number (ℝ)

Distinct123846
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2238518 × 108
Minimum0
Maximum2.4414048 × 108
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:12:18.352660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12089879
Q161271448
median1.2279194 × 108
Q31.8330837 × 108
95-th percentile2.3188178 × 108
Maximum2.4414048 × 108
Range2.4414048 × 108
Interquartile range (IQR)1.2203692 × 108

Descriptive statistics

Standard deviation70459869
Coefficient of variation (CV)0.57572222
Kurtosis-1.1992931
Mean1.2238518 × 108
Median Absolute Deviation (MAD)61031656
Skewness-0.011091131
Sum1.5232305 × 1013
Variance4.9645931 × 1015
MonotonicityNot monotonic
2023-06-22T16:12:18.457256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165048912 26
 
< 0.1%
89196552 26
 
< 0.1%
57192360 26
 
< 0.1%
169490248 23
 
< 0.1%
169467344 15
 
< 0.1%
57180136 15
 
< 0.1%
89162648 15
 
< 0.1%
165040624 15
 
< 0.1%
12194976 15
 
< 0.1%
89179832 13
 
< 0.1%
Other values (123836) 124273
99.8%
ValueCountFrequency (%)
0 11
< 0.1%
2048 1
 
< 0.1%
2056 2
 
< 0.1%
2168 1
 
< 0.1%
3784 1
 
< 0.1%
4224 1
 
< 0.1%
4480 1
 
< 0.1%
4560 1
 
< 0.1%
8280 1
 
< 0.1%
8616 1
 
< 0.1%
ValueCountFrequency (%)
244140480 1
< 0.1%
244138600 1
< 0.1%
244136552 1
< 0.1%
244135688 1
< 0.1%
244133240 1
< 0.1%
244132936 1
< 0.1%
244132752 1
< 0.1%
244131712 1
< 0.1%
244129416 1
< 0.1%
244127840 1
< 0.1%

metric5
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.223474
Minimum1
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:12:18.559421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q18
median10
Q312
95-th percentile58
Maximum98
Range97
Interquartile range (IQR)4

Descriptive statistics

Standard deviation15.944958
Coefficient of variation (CV)1.1210312
Kurtosis12.147989
Mean14.223474
Median Absolute Deviation (MAD)2
Skewness3.4831636
Sum1770282
Variance254.24168
MonotonicityNot monotonic
2023-06-22T16:12:18.660911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 22142
17.8%
9 13595
10.9%
11 12784
10.3%
10 11475
9.2%
7 11271
9.1%
12 9835
7.9%
6 8542
 
6.9%
13 6005
 
4.8%
14 3517
 
2.8%
5 3428
 
2.8%
Other values (50) 21868
17.6%
ValueCountFrequency (%)
1 173
 
0.1%
2 203
 
0.2%
3 815
 
0.7%
4 933
 
0.7%
5 3428
 
2.8%
6 8542
 
6.9%
7 11271
9.1%
8 22142
17.8%
9 13595
10.9%
10 11475
9.2%
ValueCountFrequency (%)
98 224
 
0.2%
95 672
0.5%
94 224
 
0.2%
92 448
0.4%
91 215
 
0.2%
90 357
0.3%
89 224
 
0.2%
78 224
 
0.2%
70 224
 
0.2%
68 448
0.4%

metric6
Real number (ℝ)

Distinct44809
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean260148.77
Minimum8
Maximum689161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:12:18.761545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile46
Q1221447.25
median249794
Q3310234.25
95-th percentile443124.6
Maximum689161
Range689153
Interquartile range (IQR)88787

Descriptive statistics

Standard deviation99150.928
Coefficient of variation (CV)0.38113165
Kurtosis1.9082846
Mean260148.77
Median Absolute Deviation (MAD)35370
Skewness-0.37496442
Sum3.2378636 × 1010
Variance9.8309065 × 109
MonotonicityNot monotonic
2023-06-22T16:12:18.859596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 777
 
0.6%
44 708
 
0.6%
27 636
 
0.5%
26 520
 
0.4%
29 441
 
0.4%
36 337
 
0.3%
35 290
 
0.2%
52 282
 
0.2%
45 246
 
0.2%
28 216
 
0.2%
Other values (44799) 120009
96.4%
ValueCountFrequency (%)
8 19
 
< 0.1%
9 172
0.1%
12 51
 
< 0.1%
18 36
 
< 0.1%
19 30
 
< 0.1%
20 6
 
< 0.1%
21 58
 
< 0.1%
23 71
 
0.1%
24 123
0.1%
25 184
0.1%
ValueCountFrequency (%)
689161 1
< 0.1%
689062 1
< 0.1%
689035 1
< 0.1%
688964 1
< 0.1%
688952 2
< 0.1%
687901 1
< 0.1%
687802 1
< 0.1%
687775 1
< 0.1%
687706 1
< 0.1%
687694 2
< 0.1%

DaysRunning
Real number (ℝ)

Distinct303
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.09326
Minimum0
Maximum303
Zeros1169
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:12:18.962317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q139
median85
Q3167
95-th percentile250
Maximum303
Range303
Interquartile range (IQR)128

Descriptive statistics

Standard deviation78.346307
Coefficient of variation (CV)0.74549318
Kurtosis-0.78685584
Mean105.09326
Median Absolute Deviation (MAD)58
Skewness0.55410834
Sum13080117
Variance6138.1438
MonotonicityNot monotonic
2023-06-22T16:12:19.060748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1169
 
0.9%
2 1168
 
0.9%
1 1168
 
0.9%
3 1167
 
0.9%
4 1166
 
0.9%
5 1059
 
0.9%
6 799
 
0.6%
7 757
 
0.6%
8 757
 
0.6%
10 756
 
0.6%
Other values (293) 114496
92.0%
ValueCountFrequency (%)
0 1169
0.9%
1 1168
0.9%
2 1168
0.9%
3 1167
0.9%
4 1166
0.9%
5 1059
0.9%
6 799
0.6%
7 757
0.6%
8 757
0.6%
9 756
0.6%
ValueCountFrequency (%)
303 31
< 0.1%
302 31
< 0.1%
301 31
< 0.1%
299 31
< 0.1%
298 32
< 0.1%
297 32
< 0.1%
296 32
< 0.1%
295 32
< 0.1%
294 69
0.1%
293 69
0.1%

D__S1F0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
91304 
1
33158 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 91304
73.4%
1 33158
 
26.6%

Length

2023-06-22T16:12:19.150992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:19.233142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 91304
73.4%
1 33158
 
26.6%

Most occurring characters

ValueCountFrequency (%)
0 91304
73.4%
1 33158
 
26.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91304
73.4%
1 33158
 
26.6%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91304
73.4%
1 33158
 
26.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91304
73.4%
1 33158
 
26.6%

D__S1F1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
102775 
1
21687 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 102775
82.6%
1 21687
 
17.4%

Length

2023-06-22T16:12:19.302803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:19.385535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 102775
82.6%
1 21687
 
17.4%

Most occurring characters

ValueCountFrequency (%)
0 102775
82.6%
1 21687
 
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 102775
82.6%
1 21687
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 102775
82.6%
1 21687
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 102775
82.6%
1 21687
 
17.4%

D__W1F0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
101178 
1
23284 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 101178
81.3%
1 23284
 
18.7%

Length

2023-06-22T16:12:19.455922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:19.537648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 101178
81.3%
1 23284
 
18.7%

Most occurring characters

ValueCountFrequency (%)
0 101178
81.3%
1 23284
 
18.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 101178
81.3%
1 23284
 
18.7%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 101178
81.3%
1 23284
 
18.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 101178
81.3%
1 23284
 
18.7%

D__W1F1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
104488 
1
19974 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 104488
84.0%
1 19974
 
16.0%

Length

2023-06-22T16:12:19.607485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:19.688528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 104488
84.0%
1 19974
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0 104488
84.0%
1 19974
 
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 104488
84.0%
1 19974
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 104488
84.0%
1 19974
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 104488
84.0%
1 19974
 
16.0%

D__Z1F0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
105602 
1
18860 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 105602
84.8%
1 18860
 
15.2%

Length

2023-06-22T16:12:19.758236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:19.838899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 105602
84.8%
1 18860
 
15.2%

Most occurring characters

ValueCountFrequency (%)
0 105602
84.8%
1 18860
 
15.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 105602
84.8%
1 18860
 
15.2%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 105602
84.8%
1 18860
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 105602
84.8%
1 18860
 
15.2%

D__Z1F1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
117214 
1
 
7248

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 117214
94.2%
1 7248
 
5.8%

Length

2023-06-22T16:12:19.908548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:19.989069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 117214
94.2%
1 7248
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 117214
94.2%
1 7248
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 117214
94.2%
1 7248
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 117214
94.2%
1 7248
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 117214
94.2%
1 7248
 
5.8%

D__Z1F2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
124211 
1
 
251

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 124211
99.8%
1 251
 
0.2%

Length

2023-06-22T16:12:20.057819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:20.139558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 124211
99.8%
1 251
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 124211
99.8%
1 251
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124211
99.8%
1 251
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 124211
99.8%
1 251
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124211
99.8%
1 251
 
0.2%

DoW_0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
106607 
1
17855 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106607
85.7%
1 17855
 
14.3%

Length

2023-06-22T16:12:20.206035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:20.287638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106607
85.7%
1 17855
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 106607
85.7%
1 17855
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106607
85.7%
1 17855
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106607
85.7%
1 17855
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106607
85.7%
1 17855
 
14.3%

DoW_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
106928 
1
17534 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106928
85.9%
1 17534
 
14.1%

Length

2023-06-22T16:12:20.357151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:20.440273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106928
85.9%
1 17534
 
14.1%

Most occurring characters

ValueCountFrequency (%)
0 106928
85.9%
1 17534
 
14.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106928
85.9%
1 17534
 
14.1%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106928
85.9%
1 17534
 
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106928
85.9%
1 17534
 
14.1%

DoW_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
107326 
1
17136 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 107326
86.2%
1 17136
 
13.8%

Length

2023-06-22T16:12:20.508792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:20.590472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 107326
86.2%
1 17136
 
13.8%

Most occurring characters

ValueCountFrequency (%)
0 107326
86.2%
1 17136
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 107326
86.2%
1 17136
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 107326
86.2%
1 17136
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 107326
86.2%
1 17136
 
13.8%

DoW_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
106321 
1
18141 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 106321
85.4%
1 18141
 
14.6%

Length

2023-06-22T16:12:20.658664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:20.740220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106321
85.4%
1 18141
 
14.6%

Most occurring characters

ValueCountFrequency (%)
0 106321
85.4%
1 18141
 
14.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106321
85.4%
1 18141
 
14.6%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106321
85.4%
1 18141
 
14.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106321
85.4%
1 18141
 
14.6%

DoW_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
106422 
1
18040 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106422
85.5%
1 18040
 
14.5%

Length

2023-06-22T16:12:20.808479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:20.889828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106422
85.5%
1 18040
 
14.5%

Most occurring characters

ValueCountFrequency (%)
0 106422
85.5%
1 18040
 
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106422
85.5%
1 18040
 
14.5%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106422
85.5%
1 18040
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106422
85.5%
1 18040
 
14.5%

DoW_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
106565 
1
17897 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106565
85.6%
1 17897
 
14.4%

Length

2023-06-22T16:12:20.958342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:21.038585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106565
85.6%
1 17897
 
14.4%

Most occurring characters

ValueCountFrequency (%)
0 106565
85.6%
1 17897
 
14.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106565
85.6%
1 17897
 
14.4%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106565
85.6%
1 17897
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106565
85.6%
1 17897
 
14.4%

DoW_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
106603 
1
17859 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106603
85.7%
1 17859
 
14.3%

Length

2023-06-22T16:12:21.108982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:21.190635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 106603
85.7%
1 17859
 
14.3%

Most occurring characters

ValueCountFrequency (%)
0 106603
85.7%
1 17859
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106603
85.7%
1 17859
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106603
85.7%
1 17859
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106603
85.7%
1 17859
 
14.3%

MoW_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
91935 
1
32527 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 91935
73.9%
1 32527
 
26.1%

Length

2023-06-22T16:12:21.260392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:21.340823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 91935
73.9%
1 32527
 
26.1%

Most occurring characters

ValueCountFrequency (%)
0 91935
73.9%
1 32527
 
26.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91935
73.9%
1 32527
 
26.1%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 91935
73.9%
1 32527
 
26.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 91935
73.9%
1 32527
 
26.1%

MoW_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
94933 
1
29529 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 94933
76.3%
1 29529
 
23.7%

Length

2023-06-22T16:12:21.411243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:21.492030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 94933
76.3%
1 29529
 
23.7%

Most occurring characters

ValueCountFrequency (%)
0 94933
76.3%
1 29529
 
23.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94933
76.3%
1 29529
 
23.7%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 94933
76.3%
1 29529
 
23.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 94933
76.3%
1 29529
 
23.7%

MoW_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
96474 
1
27988 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 96474
77.5%
1 27988
 
22.5%

Length

2023-06-22T16:12:21.562398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:21.643272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 96474
77.5%
1 27988
 
22.5%

Most occurring characters

ValueCountFrequency (%)
0 96474
77.5%
1 27988
 
22.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 96474
77.5%
1 27988
 
22.5%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 96474
77.5%
1 27988
 
22.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 96474
77.5%
1 27988
 
22.5%

MoW_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
98025 
1
26437 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 98025
78.8%
1 26437
 
21.2%

Length

2023-06-22T16:12:21.712280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:21.795747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 98025
78.8%
1 26437
 
21.2%

Most occurring characters

ValueCountFrequency (%)
0 98025
78.8%
1 26437
 
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98025
78.8%
1 26437
 
21.2%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98025
78.8%
1 26437
 
21.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98025
78.8%
1 26437
 
21.2%

MoW_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0
116481 
1
 
7981

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters124462
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 116481
93.6%
1 7981
 
6.4%

Length

2023-06-22T16:12:21.865550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:21.947398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 116481
93.6%
1 7981
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 116481
93.6%
1 7981
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 124462
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 116481
93.6%
1 7981
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common 124462
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 116481
93.6%
1 7981
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 124462
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 116481
93.6%
1 7981
 
6.4%

metric2_log
Real number (ℝ)

Distinct524
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.016333308
Minimum0
Maximum0.3186477
Zeros118082
Zeros (%)94.9%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:12:22.026460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.31832511
Maximum0.3186477
Range0.3186477
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.070267972
Coefficient of variation (CV)4.3021274
Kurtosis14.562817
Mean0.016333308
Median Absolute Deviation (MAD)0
Skewness4.0697153
Sum2032.8762
Variance0.0049375878
MonotonicityNot monotonic
2023-06-22T16:12:22.130692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118082
94.9%
0.3186477013 281
 
0.2%
0.3183251136 260
 
0.2%
0.3186346333 254
 
0.2%
0.3186449345 201
 
0.2%
0.3186477013 175
 
0.1%
0.3186476995 169
 
0.1%
0.3186038644 166
 
0.1%
0.3186474583 152
 
0.1%
0.3186477005 140
 
0.1%
Other values (514) 4582
 
3.7%
ValueCountFrequency (%)
0 118082
94.9%
0.3183251136 260
 
0.2%
0.3186038644 166
 
0.1%
0.3186346333 254
 
0.2%
0.3186422334 132
 
0.1%
0.3186449345 201
 
0.2%
0.3186461199 90
 
0.1%
0.3186466613 1
 
< 0.1%
0.3186467174 103
 
0.1%
0.3186470498 26
 
< 0.1%
ValueCountFrequency (%)
0.3186477013 71
0.1%
0.3186477013 5
 
< 0.1%
0.3186477013 4
 
< 0.1%
0.3186477013 1
 
< 0.1%
0.3186477013 2
 
< 0.1%
0.3186477013 1
 
< 0.1%
0.3186477013 22
 
< 0.1%
0.3186477013 26
 
< 0.1%
0.3186477013 36
< 0.1%
0.3186477013 1
 
< 0.1%

metric3_log
Real number (ℝ)

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013358984
Minimum0
Maximum0.18364369
Zeros115331
Zeros (%)92.7%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:12:22.232311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.17942897
Maximum0.18364369
Range0.18364369
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.047480706
Coefficient of variation (CV)3.5542152
Kurtosis8.7170137
Mean0.013358984
Median Absolute Deviation (MAD)0
Skewness3.2733765
Sum1662.6859
Variance0.0022544174
MonotonicityNot monotonic
2023-06-22T16:12:22.326552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 115331
92.7%
0.1794289688 3273
 
2.6%
0.1831803588 749
 
0.6%
0.1836436933 434
 
0.3%
0.1836414733 298
 
0.2%
0.1836436926 293
 
0.2%
0.1836330596 278
 
0.2%
0.1836436843 269
 
0.2%
0.1836149951 267
 
0.2%
0.1836430347 262
 
0.2%
Other values (33) 3008
 
2.4%
ValueCountFrequency (%)
0 115331
92.7%
0.1794289688 3273
 
2.6%
0.1831803588 749
 
0.6%
0.1835469631 113
 
0.1%
0.1836149951 267
 
0.2%
0.1836330596 278
 
0.2%
0.1836414733 298
 
0.2%
0.1836425243 251
 
0.2%
0.1836430347 262
 
0.2%
0.1836433014 241
 
0.2%
ValueCountFrequency (%)
0.1836436933 434
0.3%
0.1836436933 84
 
0.1%
0.1836436933 5
 
< 0.1%
0.1836436933 1
 
< 0.1%
0.1836436933 6
 
< 0.1%
0.1836436933 5
 
< 0.1%
0.1836436933 177
0.1%
0.1836436933 3
 
< 0.1%
0.1836436933 6
 
< 0.1%
0.1836436933 84
 
0.1%

metric4_log
Real number (ℝ)

Distinct98
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012289878
Minimum0
Maximum0.16415904
Zeros115130
Zeros (%)92.5%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:12:22.431394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.16415787
Maximum0.16415904
Range0.16415904
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.04316786
Coefficient of variation (CV)3.5124726
Kurtosis8.4195672
Mean0.012289878
Median Absolute Deviation (MAD)0
Skewness3.2278711
Sum1529.6228
Variance0.0018634641
MonotonicityNot monotonic
2023-06-22T16:12:22.528930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 115130
92.5%
0.164157868 3680
 
3.0%
0.1617519345 889
 
0.7%
0.1639554215 710
 
0.6%
0.1641237395 466
 
0.4%
0.1641590085 453
 
0.4%
0.1641499701 358
 
0.3%
0.164158961 294
 
0.2%
0.1641590354 245
 
0.2%
0.1641560497 231
 
0.2%
Other values (88) 2006
 
1.6%
ValueCountFrequency (%)
0 115130
92.5%
0.1617519345 889
 
0.7%
0.1639554215 710
 
0.6%
0.1641237395 466
 
0.4%
0.1641499701 358
 
0.3%
0.1641560497 231
 
0.2%
0.164157868 3680
 
3.0%
0.1641585178 174
 
0.1%
0.1641587828 170
 
0.1%
0.1641589025 45
 
< 0.1%
ValueCountFrequency (%)
0.1641590354 71
0.1%
0.1641590354 5
 
< 0.1%
0.1641590354 12
 
< 0.1%
0.1641590354 3
 
< 0.1%
0.1641590354 1
 
< 0.1%
0.1641590354 2
 
< 0.1%
0.1641590354 4
 
< 0.1%
0.1641590354 1
 
< 0.1%
0.1641590354 1
 
< 0.1%
0.1641590354 53
< 0.1%

metric7_log
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0.0
123007 
0.031485000271344425
 
1455

Length

Max length20
Median length3
Mean length3.1987354
Min length3

Characters and Unicode

Total characters398121
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 123007
98.8%
0.031485000271344425 1455
 
1.2%

Length

2023-06-22T16:12:22.776900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:22.866255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 123007
98.8%
0.031485000271344425 1455
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 253289
63.6%
. 124462
31.3%
4 5820
 
1.5%
3 2910
 
0.7%
1 2910
 
0.7%
5 2910
 
0.7%
2 2910
 
0.7%
8 1455
 
0.4%
7 1455
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 273659
68.7%
Other Punctuation 124462
31.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 253289
92.6%
4 5820
 
2.1%
3 2910
 
1.1%
1 2910
 
1.1%
5 2910
 
1.1%
2 2910
 
1.1%
8 1455
 
0.5%
7 1455
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 124462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 398121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 253289
63.6%
. 124462
31.3%
4 5820
 
1.5%
3 2910
 
0.7%
1 2910
 
0.7%
5 2910
 
0.7%
2 2910
 
0.7%
8 1455
 
0.4%
7 1455
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 398121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 253289
63.6%
. 124462
31.3%
4 5820
 
1.5%
3 2910
 
0.7%
1 2910
 
0.7%
5 2910
 
0.7%
2 2910
 
0.7%
8 1455
 
0.4%
7 1455
 
0.4%

metric8_log
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size972.5 KiB
0.0
123007 
0.031485000271344425
 
1455

Length

Max length20
Median length3
Mean length3.1987354
Min length3

Characters and Unicode

Total characters398121
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 123007
98.8%
0.031485000271344425 1455
 
1.2%

Length

2023-06-22T16:12:22.939990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-22T16:12:23.028133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 123007
98.8%
0.031485000271344425 1455
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 253289
63.6%
. 124462
31.3%
4 5820
 
1.5%
3 2910
 
0.7%
1 2910
 
0.7%
5 2910
 
0.7%
2 2910
 
0.7%
8 1455
 
0.4%
7 1455
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 273659
68.7%
Other Punctuation 124462
31.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 253289
92.6%
4 5820
 
2.1%
3 2910
 
1.1%
1 2910
 
1.1%
5 2910
 
1.1%
2 2910
 
1.1%
8 1455
 
0.5%
7 1455
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 124462
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 398121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 253289
63.6%
. 124462
31.3%
4 5820
 
1.5%
3 2910
 
0.7%
1 2910
 
0.7%
5 2910
 
0.7%
2 2910
 
0.7%
8 1455
 
0.4%
7 1455
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 398121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 253289
63.6%
. 124462
31.3%
4 5820
 
1.5%
3 2910
 
0.7%
1 2910
 
0.7%
5 2910
 
0.7%
2 2910
 
0.7%
8 1455
 
0.4%
7 1455
 
0.4%

metric9_log
Real number (ℝ)

Distinct66
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.093933975
Minimum0
Maximum0.48180726
Zeros97332
Zeros (%)78.2%
Negative0
Negative (%)0.0%
Memory size972.5 KiB
2023-06-22T16:12:23.106544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.47903386
Maximum0.48180726
Range0.48180726
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.17936995
Coefficient of variation (CV)1.9095322
Kurtosis0.10491434
Mean0.093933975
Median Absolute Deviation (MAD)0
Skewness1.4223626
Sum11691.21
Variance0.03217358
MonotonicityNot monotonic
2023-06-22T16:12:23.210311image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 97332
78.2%
0.3674983883 9434
 
7.6%
0.43253537 3721
 
3.0%
0.4546874576 2327
 
1.9%
0.4647406228 1395
 
1.1%
0.4733182619 797
 
0.6%
0.4753730825 774
 
0.6%
0.4701174951 735
 
0.6%
0.4767684848 733
 
0.6%
0.4784849275 640
 
0.5%
Other values (56) 6574
 
5.3%
ValueCountFrequency (%)
0 97332
78.2%
0.3674983883 9434
 
7.6%
0.43253537 3721
 
3.0%
0.4546874576 2327
 
1.9%
0.4647406228 1395
 
1.1%
0.4701174951 735
 
0.6%
0.4733182619 797
 
0.6%
0.4753730825 774
 
0.6%
0.4767684848 733
 
0.6%
0.477758198 335
 
0.3%
ValueCountFrequency (%)
0.481807261 5
 
< 0.1%
0.4818072593 4
 
< 0.1%
0.481807257 5
 
< 0.1%
0.4818072278 6
 
< 0.1%
0.4818072235 84
0.1%
0.4818072198 179
0.1%
0.4818072096 5
 
< 0.1%
0.4818072095 1
 
< 0.1%
0.4818071832 5
 
< 0.1%
0.4818070538 118
0.1%

Interactions

2023-06-22T16:12:15.620417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:10.689438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:11.393260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:12.076920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:12.794245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:13.491619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:14.194995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:14.914381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:15.706111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:10.780478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:11.477604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:12.167275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:12.881044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:13.579299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:14.284241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:15.002626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:15.787825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:10.864075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:11.557891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:12.252261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:12.963535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:13.662675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:14.370069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:15.086999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:15.877386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:10.957946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:11.647123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:12.343616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:13.054566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:13.753460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:14.463839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:15.178985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:15.964608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:11.043736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:11.731960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:12.431693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:13.138626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:13.840860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:14.552784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:15.265800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:16.053029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:11.131535image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:11.818971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:12.523052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:13.227531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:13.929322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:14.643268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:15.356676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:16.142815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:11.221472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:11.907584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:12.617318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:13.318902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:14.020976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:14.737077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:15.446450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:16.230370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:11.309422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:11.993969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:12.707668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:13.407694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:14.109982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:14.827652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-22T16:12:15.534149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-22T16:12:23.315227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
metric1metric5metric6DaysRunningmetric2_logmetric3_logmetric4_logmetric9_logfailureD__S1F0D__S1F1D__W1F0D__W1F1D__Z1F0D__Z1F1D__Z1F2DoW_0DoW_1DoW_2DoW_3DoW_4DoW_5DoW_6MoW_1MoW_2MoW_3MoW_4MoW_5metric7_logmetric8_log
metric11.000-0.005-0.003-0.005-0.0010.0020.002-0.0030.0090.0030.0030.0000.0040.0040.0030.0000.0060.0010.0000.0080.0050.0020.0090.0000.0000.0000.0000.0020.0000.000
metric5-0.0051.0000.083-0.010-0.0270.107-0.0210.0340.0070.1270.1800.1890.1330.2080.1180.0370.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0060.0080.0280.028
metric6-0.0030.0831.0000.186-0.0780.0700.0120.0900.0120.1870.2210.1810.4130.3030.2040.1070.0000.0000.0140.0050.0000.0000.0000.0880.0300.0300.0340.0350.0970.097
DaysRunning-0.005-0.0100.1861.000-0.0210.002-0.021-0.0200.0130.0650.0540.0680.0370.0330.0440.0410.0440.0460.0540.0400.0370.0410.0440.1010.0480.0370.0610.1290.0710.071
metric2_log-0.001-0.027-0.078-0.0211.000-0.0190.225-0.0290.0520.0120.0420.0150.0930.0500.0040.0100.0000.0000.0000.0000.0000.0000.0000.0090.0000.0000.0040.0030.1070.107
metric3_log0.0020.1070.0700.002-0.0191.0000.1210.3900.0000.0000.1030.0630.0160.0010.0080.1020.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0080.008
metric4_log0.002-0.0210.012-0.0210.2250.1211.0000.0490.0560.0580.1100.0880.0290.0440.0380.0120.0000.0000.0040.0000.0000.0000.0000.0190.0050.0040.0060.0030.1600.160
metric9_log-0.0030.0340.090-0.020-0.0290.3900.0491.0000.0040.0850.1090.0850.0870.0420.0480.0230.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0210.021
failure0.0090.0070.0120.0130.0520.0000.0560.0041.0000.0040.0080.0040.0000.0000.0000.0000.0080.0000.0000.0040.0000.0040.0080.0030.0000.0050.0000.0000.0950.095
D__S1F00.0030.1270.1870.0650.0120.0000.0580.0850.0041.0000.2770.2890.2630.2550.1500.0270.0000.0000.0000.0000.0000.0000.0000.0080.0000.0010.0000.0090.0270.027
D__S1F10.0030.1800.2210.0540.0420.1030.1100.1090.0080.2771.0000.2200.2010.1940.1140.0200.0000.0000.0020.0000.0000.0000.0000.0130.0020.0080.0000.0000.0450.045
D__W1F00.0000.1890.1810.0680.0150.0630.0880.0850.0040.2890.2201.0000.2100.2030.1190.0210.0000.0000.0030.0000.0000.0000.0000.0150.0020.0070.0020.0010.0350.035
D__W1F10.0040.1330.4130.0370.0930.0160.0290.0870.0000.2630.2010.2101.0000.1850.1090.0190.0000.0000.0010.0000.0000.0000.0000.0110.0010.0050.0000.0000.0120.012
D__Z1F00.0040.2080.3030.0330.0500.0010.0440.0420.0000.2550.1940.2030.1851.0000.1050.0190.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.0010.0070.0300.030
D__Z1F10.0030.1180.2040.0440.0040.0080.0380.0480.0000.1500.1140.1190.1090.1051.0000.0100.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0290.029
D__Z1F20.0000.0370.1070.0410.0100.1020.0120.0230.0000.0270.0200.0210.0190.0190.0101.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0030.003
DoW_00.0060.0000.0000.0440.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0001.0000.1660.1630.1690.1680.1680.1670.0000.0000.0000.0010.0120.0020.002
DoW_10.0010.0000.0000.0460.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1661.0000.1620.1670.1670.1660.1660.0000.0000.0000.0040.0130.0000.000
DoW_20.0000.0000.0140.0540.0000.0000.0040.0000.0000.0000.0020.0030.0010.0000.0000.0000.1630.1621.0000.1650.1640.1640.1630.0040.0040.0070.0070.0240.0000.000
DoW_30.0080.0000.0050.0400.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.1690.1670.1651.0000.1700.1690.1690.0000.0050.0000.0040.0250.0000.000
DoW_40.0050.0000.0000.0370.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1680.1670.1640.1701.0000.1690.1680.0000.0000.0050.0040.0250.0000.000
DoW_50.0020.0000.0000.0410.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.1680.1660.1640.1690.1691.0000.1680.0000.0000.0000.0070.0110.0000.000
DoW_60.0090.0000.0000.0440.0000.0000.0000.0000.0080.0000.0000.0000.0000.0000.0000.0000.1670.1660.1630.1690.1680.1681.0000.0000.0000.0000.0010.0120.0000.000
MoW_10.0000.0120.0880.1010.0090.0000.0190.0050.0030.0080.0130.0150.0110.0000.0000.0000.0000.0000.0040.0000.0000.0000.0001.0000.3320.3200.3090.1560.0030.003
MoW_20.0000.0000.0300.0480.0000.0000.0050.0000.0000.0000.0020.0020.0010.0000.0000.0000.0000.0000.0040.0050.0000.0000.0000.3321.0000.3000.2900.1460.0050.005
MoW_30.0000.0000.0300.0370.0000.0000.0040.0000.0050.0010.0080.0070.0050.0030.0000.0000.0000.0000.0070.0000.0050.0000.0000.3200.3001.0000.2800.1410.0000.000
MoW_40.0000.0060.0340.0610.0040.0000.0060.0000.0000.0000.0000.0020.0000.0010.0000.0000.0010.0040.0070.0040.0040.0070.0010.3090.2900.2801.0000.1360.0000.000
MoW_50.0020.0080.0350.1290.0030.0000.0030.0000.0000.0090.0000.0010.0000.0070.0000.0000.0120.0130.0240.0250.0250.0110.0120.1560.1460.1410.1361.0000.0000.000
metric7_log0.0000.0280.0970.0710.1070.0080.1600.0210.0950.0270.0450.0350.0120.0300.0290.0030.0020.0000.0000.0000.0000.0000.0000.0030.0050.0000.0000.0001.0001.000
metric8_log0.0000.0280.0970.0710.1070.0080.1600.0210.0950.0270.0450.0350.0120.0300.0290.0030.0020.0000.0000.0000.0000.0000.0000.0030.0050.0000.0000.0001.0001.000

Missing values

2023-06-22T16:12:16.393306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-22T16:12:16.898149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

failuredatedevicemetric1metric5metric6DaysRunningD__S1F0D__S1F1D__W1F0D__W1F1D__Z1F0D__Z1F1D__Z1F2DoW_0DoW_1DoW_2DoW_3DoW_4DoW_5DoW_6MoW_1MoW_2MoW_3MoW_4MoW_5metric2_logmetric3_logmetric4_logmetric7_logmetric8_logmetric9_log
002015-01-01S1F010852156306726407438010000000001000100000.3186470.00.1641590.00.00.475373
102015-01-01W1F0Y13C2343186404185772000100000001000100000.0000000.00.0000000.00.00.454687
202015-01-01W1F0XKWR89660704730000100000001000100000.0000000.00.0000000.00.00.000000
302015-01-01W1F0X7QX16201345612217686000100000001000100000.0000000.00.0000000.00.00.000000
402015-01-01W1F0X7PR131383929191343000100000001000100000.0000000.00.0000000.00.00.000000
502015-01-01W1F0X7P23322483213216960000100000001000100000.0000000.00.0000000.00.00.000000
602015-01-01W1F0X7111853863215217590000100000001000100000.0000000.00.0000000.00.00.000000
702015-01-01W1F0Y2PY1320330805182050000100000001000100000.0000000.00.0000000.00.00.000000
802015-01-01W1F0X70N16558355213186288000100000001000100000.0000000.00.0000000.00.00.000000
902015-01-01W1F0X6V017503132011213515000100000001000100000.0000000.00.0000000.00.00.000000
failuredatedevicemetric1metric5metric6DaysRunningD__S1F0D__S1F1D__W1F0D__W1F1D__Z1F0D__Z1F1D__Z1F2DoW_0DoW_1DoW_2DoW_3DoW_4DoW_5DoW_6MoW_1MoW_2MoW_3MoW_4MoW_5metric2_logmetric3_logmetric4_logmetric7_logmetric8_logmetric9_log
12445202015-10-31S1F0GPXY524465601135018030310000000000010000010.0000000.0000000.0000000.0000000.0000000.478485
12445302015-10-31S1F0GGPP1362521841235958230310000000000010000010.0000000.1836440.0000000.0000000.0000000.000000
12445402015-10-31S1F0GCED1832246081133947130310000000000010000010.3186480.0000000.1641590.0000000.0000000.000000
12445502015-10-31S1F0FP0C1326840641235421930310000000000010000010.0000000.0000000.0000000.0000000.0000000.000000
12445602015-10-31S1F0FGBQ1528730001230757330310000000000010000010.0000000.0000000.0000000.0000000.0000000.000000
12445702015-10-31S1F0EGMT1775450881134682930310000000000010000010.0000000.0000000.1641590.0314850.0314850.000000
12445802015-10-31S1F0E9EP1605007441135206430310000000000010000010.0000000.0000000.1641590.0000000.0000000.000000
12445902015-10-31Z1F0QL3N620181041235693730300001000000010000010.0000000.0000000.0000000.0000000.0000000.000000
12446002015-10-31W1F0FY92810328081535366130300100000000010000010.0000000.1794290.1641580.0000000.0000000.367498
12446102015-10-31Z1F0QLC1321173681035084030300001000000010000010.0000000.0000000.0000000.0000000.0000000.000000